Northern Dene Astronomical and Sky-Related Knowledge, Central Western Alaska to Great Slave Lake N.W.T. (Northwest Territories of Canada), (2018-2022).
Bibliographic record
Abstract
This data set includes primary materials collected and compiled by Chris M. Cannon between 2018-2022 as part of his NSF (National Science Foundation) supported doctoral dissertation research on Northern Dene (Athabascan) astronomical and sky-related knowledge. Additional data pertaining to this research that Cannon collected before 2018 is also archived with this data set. In total, this comparative and multi-sited anthropological study spans 11 years and includes contributions from Northern Dene Elders, speakers, and tradition bearers from 12 ethnolinguistic groups across 32 communities between central-western Alaska and the eastern end of Great Slave Lake, N.W.T. (Northwest Territories of Canada). These data include audio recordings (interviews, conversations, stories, texts, linguistic elicitation), transcribed and translated texts and stories, maps, star charts, wordlists, video recordings, photos, fieldnotes, published papers, and Cannon's PhD dissertation. Publications relating to this research acknowledge the financial support of NSF and variety of other institutions, First Nations, and Tribes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".